TUN-AI: Tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data
Fisheries Research, 2022
Abstract
This study uses data from echo-sounder buoys attached to drifting Fish Aggregating Devices (dFADs) used by tuna purse-seine fisheries to estimate tuna biomass. The researchers combined FAD logbook data, oceanographic data, and echo-sounder buoy data to train and evaluate different Machine Learning models and establish a pipeline, TUN-AI, to process this data.
A Machine Learning method, TUN-AI, for accurate tuna biomass estimation, benefiting the fishing industry, fishery management organizations, and conservation efforts.
Where does it apply?
The findings of this study are applicable primarily to the fishing industry, specifically tuna purse-seine fisheries where dFADs and echo-sounder buoys are used. They can utilize this model to more accurately estimate tuna biomass and make informed decisions about fisheries operations.
These insights could also be valuable to fishery management organizations, conservation groups, and regulatory bodies, aiding in the development of sustainable fishing policies and practices.
Furthermore, the methodology may also be relevant to related fields and industries where tracking and estimating biomass or population data of certain species is essential, like wildlife management or environmental study areas.
Why does it matters?
This study provides a more accurate and reliable method for estimating tuna biomass, towards a more sustainable fisheries operation. By applying Machine Learning models to various data types (FAD logbook data, oceanographic data, echo-sounder buoy data), the study enhances the potential for gaining valuable insights into tuna behavior and abundance. The methodology can potentially be applied to other fisheries and species, contributing to ocean conservation efforts.
TUN - AI
Fisheries Research, 2022
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